Description:

Northwestern University Transportation Center presents:

"Harnessing Game Theory & Big Data for Autonomous and Connected Transportation Ecosystem"

Xuan (Sharon) Di
Assistant Professor
Department of Civil Engineering and Engineering Mechanics
Smart Cities Center, Data Science Institute
Columbia University in the City of New York

>> REFRESHEMENTS SERVED @ 3:30PM <<

Abstract:

Emerging transportation technology is expected to revolutionize the future transportation ecosystem. However, such disruptive technology can be a double-sided sword, adversely impacting social welfare, if not properly managed or designed. My research aims to understand the traffic implications of the emerging technologies and develop optimal interventions to achieve desirable outcomes. In this talk, I will primarily focus on how I address the challenges of autonomous vehicle control in presence of human drivers, leveraging the core concepts of game theory, dynamic control, and machine learning. 

As this era’s biggest game-changer, autonomous vehicles (AV) are expected to exhibit new driving and travel behaviors, thanks to their sensing, communication, and computational capabilities. However, a majority of studies simply tailor human-driven vehicles (HV)’s behavior for AVs by tweaking some behavioral parameters. In these models, AVs are essentially human drivers but react faster, “see” farther, and “know” the road environment better. We believe AVs’ most disruptive characteristic lies in its intelligent goal-seeking and adapting behavior. Based on whether the mixed traffic environment is deterministic or stochastic, we propose two types of controls: game-based and learning-based. I will first introduce a game-theoretic decision-making process for a large number of AVs. To illustrate the potential advantage that AVs may bring to stabilize traffic, we propose a multi-class game where AVs are modeled as intelligent game-players and HVs are modeled using a classical non-equilibrium traffic flow model. I will then briefly talk about our on-going work on a learning-based control when the mixed traffic environment contains uncertainty, which allows AVs to interact with the environment and learn optimal driving policies dynamically. Imitation learning is first applied to mimic the interaction between AVs and HVs using public datasets. Then multi-agent reinforcement learning is applied for many AVs to navigate the traffic environment safely and efficiently.    

Speaker Bio:

Xuan (Sharon) Di is a tenure-track Assistant Professor in the Department of Civil Engineering and Engineering Mechanics at Columbia University in the City of New York since September 2016 and serves on a committee for the Smart Cities Center in the Data Science Institute. Prior to joining Columbia, she was a Postdoctoral Research Fellow at the University of Michigan Transportation Research Institute (UMTRI). She received her Ph.D. degree from the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, Twin Cities in 2014. Dr. Di received a number of awards including the Transportation Data Analytics Contest Winner from Transportation Research Board (TRB), the Dafermos Best Paper Award Honorable Mention from the TRB Network Modeling Committee, Outstanding Presentation Award from INFORMS, and the Best Paper Award and Best Graduate Student Scholarship from North-Central Section Institute of Transportation Engineers (ITE). She also serves as the reviewer for a number of journals, including Transportation Science, Transportation Research Part B/C/D, European Journal of Operational Research, Networks and Spatial Economics, and Transportation.

Dr. Di directs the DitecT (Data and innovative technology-driven Transportation) Lab @ Columbia University. Her research lies at the intersection of game theory, dynamic control, and machine learning. She is specialized in emerging transportation systems optimization and control, shared mobility modeling, and data-driven urban mobility analysis. Details about DitecT Lab and Prof. Sharon Di’s research can be found in the following link: http://sharondi-columbia.wixsite.com/ditectlab.

...

Parking & Public Transit:
Parking @ Northwestern University
Permits Required: Monday - Friday, 8am - 4pm
2-hour-limit parking on Foster Street
Closest El / CTA Stop: Foster - Purple Line
Closest Metra Station: Davis Street/Evanston, Union Pacific North line 

Remote Streaming & Dial-In:
***Join by computer or phone***

Streaming:
https://bluejeans.com/8474917287
Meeting ID: 847 491 7287

Dial-In:
+1.408.740.7256 (US (San Jose))
+1.888.240.2560 (US Toll Free)
+1.408.317.9253 (US (Primary, San Jose))
(Global Numbers)
Meeting ID: 847 491 7287